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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and deploying AI-powered applications and services, with a strong focus on integrating ML models and LLMs into production environments. Proficient in managing the entire lifecycle of AI systems, including validation, monitoring, and collaboration with cross-functional teams.
Highest-signal resume keywords
AI Application DevelopmentML Model IntegrationAPI DesignCloud Data ManagementCross-Functional Collaboration
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine LearningLarge Language ModelsWorkflow AutomationAutomated TestingModel ValidationCI/CDVersion ControlData EvaluationPredictive ModelingAPI Integration
Soft Skills
Clear CommunicationIndependent WorkJudgment with DataClient EngagementProblem Solving
Tools & Technologies
AWS S3AWS GlueAWS EMRAWS MWAAAWS SageMaker
Industry Keywords
Life SciencesPharmaceutical AnalyticsCapital Markets
Tech Stack
Tools & technologiesAWSCloud
About the role
Key responsibilities & impact- Design, build, and deploy AI-powered applications, services, and data workflows
- Work hands-on with engineers, data scientists, and domain experts
- Own implementation from prototype through production
- Design and implement systems combining ML models, LLMs, retrieval, and workflow automation for life sciences market questions
- Make practical choices about model quality, reliability, latency, and cost
- Build automated tests, evaluation datasets, and monitoring for AI outputs and application reliability
- Maintain rigorous validation of predictive models, including point-in-time backtesting, baseline comparisons, and checks for leakage and coverage bias
- Take prototypes into production and own APIs, integrations, deployment, observability, and incident resolution
- Build reusable capabilities that scale across therapies, companies, and clients
- Work directly with clients and domain experts to understand analytical workflows, explain findings, challenge assumptions, and turn feedback into product priorities
Requirements
What you’ll need- Track record of independently shipping applied AI applications or services, from initial prototype to production use
- Practical experience integrating ML models and LLMs into software, including retrieval, tool use, structured outputs, and evaluation
- Sound judgment about model limitations and when a simpler approach is sufficient
- Ability to run working sessions with clients’ technical and non-technical stakeholders and identify impactful solutions
- Strong software engineering fundamentals, including API design, testing, version control, and maintainable code
- Experience deploying and operating AI systems, including CI/CD, model versioning, monitoring, and troubleshooting
- Ability to work with large datasets in the cloud, ideally using AWS services such as S3, Glue, EMR, MWAA, and SageMaker
- Sound judgment with incomplete or noisy data
- Clear communication and ability to work independently within a cross-functional squad and with client stakeholders
- Background in life sciences, pharmaceutical analytics, or capital markets helpful but not required